134 results about "Risk impact" patented technology

The invention provides an enterprise data risk processing method and system based on a dynamic knowledge graph, and the method comprises the steps: collecting a real-time data flow generated in the operation process of an enterprise, extracting an entity object and an incidence relation in the real-time data flow, and forming an initial node and an initial edge; and receiving a risk processing request, positioning an initial node corresponding to the target entity from the dynamic knowledge graph according to the risk attribute identifier, and determining an association propagation path generation rule of the target entity based on the risk influence range description. And calling a pre-trained risk propagation model, traversing adjacent nodes in the dynamic knowledge graph according to a correlation propagation path generation rule by taking the initial node as a starting point, and generating a risk propagation path set corresponding to the target entity. And calculating a comprehensive risk value corresponding to the target entity according to the node connection density and the risk attribute superposition intensity of each risk propagation path, and triggering a risk early warning signal when the comprehensive risk value exceeds a preset threshold. The enterprise risk prevention and control initiative and decision-making accuracy can be improved.

The invention discloses a digital twinning-based major engineering construction safety active control method, system and equipment, and belongs to the technical field of construction active control. The problems of low safety risk identification efficiency and inaccurate risk control means in the major engineering construction process are solved, major engineering construction site environment and structural feature data are obtained through space-air-ground-water multi-way intelligent means and equipment, a digital twinborn model of a real-time mapping construction site is established, and the safety risk identification efficiency of the major engineering construction site is improved. Correcting the digital twinborn model by using real-time data; a digital twinborn model is used as a basic model of finite element analysis, mechanical properties of different construction stages are analyzed, dynamic early warning is performed on an analysis result in combination with real-time data, a safety risk derivation model is established to perform real-time analysis on key problems such as a risk point location, a risk influence range and risk derivation prediction in the digital twinborn model, and the safety risk derivation prediction result is obtained. Data support is provided for engineering safety active control, an active control strategy model is established, a control strategy is output to a servo control device according to risk information, strategy dynamic adjustment is performed according to a real-time effect, and high-precision cooperative control of major engineering safety monitoring is realized.

The invention relates to the technical field of risk analysis, in particular to a supply chain risk quantitative evaluation method and system based on a dynamic affair atlas, and the method comprises the steps: collecting multi-source heterogeneous data, constructing a four-dimensional space-time model comprising a time dimension, a geographic space dimension, a supply chain network space dimension and a risk influence space dimension, representing the supply chain event as four-dimensional spatio-temporal data; a supply chain entity is identified from the four-dimensional spatio-temporal data, risk events are extracted, a affair graph is constructed, and the affair graph takes the risk events as nodes and the evolution relation between the events as edges to calculate the relation weight between the events; calculating a probability quantized value of the risk conduction path based on the affair map, and obtaining a comprehensive risk score of the target entity; generating a risk mitigation strategy based on the comprehensive risk score; and monitoring the deviation between the actual risk occurrence condition and the prediction result, and updating the affair map and the risk mitigation strategy through adaptive parameter optimization and an incremental learning mechanism to form a self-evolutionary risk assessment system.

The invention relates to a risk analysis method, and particularly provides a dynamic risk analysis method based on a Bayesian network. Converting the risk influence factors into a first group of network nodes based on a historical risk knowledge model, and constructing an initial topological structure according to a knowledge model logic relationship; secondly, acquiring multi-source real-time monitoring data, learning correlation among variables through a data mining algorithm, and generating a second group of nodes and a data-driven topological structure; then, connecting the historical information Bayesian network with the data-driven Bayesian network through a shared risk node, and constructing a comprehensive Bayesian network; meanwhile, a dynamic probability updating mechanism is established, and comprehensive network probability parameters are dynamically adjusted by adopting a weighted fusion algorithm in combination with a historical prior probability and a real-time posterior probability; and finally, performing risk reasoning based on the dynamically updated integrated network, and outputting risk quantitative indexes.

The invention relates to the technical field of supply chain management, in particular to a risk control method and system for a supply chain. According to the method, the limitation of a traditional method in processing complex data can be overcome, the inventory-production data of each link of the supply chain is acquired in real time, the risk influence index is dynamically calculated in combination with internal and external influence factors, potential risks can be comprehensively identified and dynamically assessed, and the risk assessment efficiency is improved. According to the method, coping strategies can be adjusted in time according to market demand fluctuation, policy change and other external factors, the response capability and adaptability of the supply chain are improved, the effectiveness of supply chain risk control is ensured through real-time monitoring of the risk control deviation degree, the defect that in-time early warning and adjustment are lacked in a traditional method is overcome, and the risk control efficiency is improved. Serious consequences of supply chain interruption, cost increase and the like are effectively prevented, the accuracy, the flexibility and the predictability of supply chain risk management are improved, and the supply chain risk management method has remarkable practical application value.

The invention provides an interaction method and system for thromboembolism risk assessment, and can be applied to the technical field of artificial intelligence. The method comprises the steps that a first server side responds to an inquiry request sent by a first terminal, and inquiry diagnosis data and historical clinical data are sent to a second server side; the second server obtains an initial risk assessment score according to the inquiry diagnosis data and the historical clinical data; under the condition that the initial risk assessment score is greater than a preset risk threshold value, the second server side sends inquiry diagnosis data and historical clinical data to a second terminal; the second terminal determines risk influence factors according to the inquiry diagnosis data and the historical clinical data; the second server obtains the detection result, and performs hospital thromboembolism risk assessment on the detection data by using a Bayesian network to obtain a target risk assessment result; and the second server determines a rehabilitation guidance strategy according to the target risk assessment result and the discharge diagnosis result, and sends the rehabilitation guidance strategy to the first terminal.

The invention provides a level protection-oriented network security configuration checking method, which comprises the following steps of: constructing a knowledge graph associated with a security control item and a technical implementation mode by relying on a standard document and configuration data, and implementing a control item logic dependency and semantic weight reconstruction model; security configuration of assets is automatically collected and semantically mapped to map nodes, configuration deviation is recognized, a risk influence path template is matched in combination with asset importance and service context, semantic propagation path influence intensity is dynamically calculated, and an accumulated risk influence value of configuration deviation is generated in an aggregation mode; according to the method, risk level judgment is carried out by adopting a piecewise nonlinear function, a structured risk assessment record is output, risk identification and reinforcement suggestion closed loop are realized, and the hierarchical management and control and automatic compliance response capability of security configuration deviation in a network environment is improved.

The invention discloses a project combination adjusting method, and particularly relates to a project combination adjusting method and device based on dynamic risks and electronic equipment. The method comprises the following steps: acquiring support degree information corresponding to an existing project combination at a moment t-1, and acquiring risk influence information at a moment t; updating the support degree information based on the risk influence information to obtain updated support degree information at the moment t; judging whether the existing project combination meets a preset constraint condition or not based on the updated support degree information at the moment t; under the condition that the existing project combination does not meet the preset constraint condition, the adjustment cost corresponding to each project adjustment scheme is calculated, and the project adjustment scheme comprises an adjustment strategy executed by each existing project; and adjusting the existing project combination based on the project adjustment scheme with the lowest adjustment cost to obtain an updated project combination at the moment t. According to the method, the project combination can be dynamically adjusted according to the risk impact, and efficient allocation of resources is realized.

The invention discloses a wharf risk assessment and management method based on artificial intelligence, particularly relates to the field of wharf risk assessment and management, and is used for solving the problems that an existing wharf is difficult to quantify and delay influence on a supply chain network level, and prospective intervention cannot be performed on supply chain risks. According to the method, a comprehensive data platform of wharf operation and supply chain cargo linkage is constructed, and the bottleneck time point of a future delivery window is analyzed and predicted based on the ship arrival density, the operation bearing capacity and the stockpiling structure; searching a historical supply chain interruption event and extracting corresponding cargo and node information; tracking a conduction path of an interruption event in the supply chain network, quantifying the influence of a bottleneck on a downstream node, and establishing a supply chain risk influence model; generating a supply chain risk influence map in combination with future bottleneck prediction; and finally, a composite risk portrait is constructed, and targeted risk intervention is carried out on the wharf and the supply chain node. And intelligent prediction and management of wharf and supply chain full-chain risks are realized.

The invention discloses a user big data processing method based on supply chain finance, and the method comprises the steps: collecting user interaction data, extracting short-term time sequence and long-term behavior features to construct an interaction data network, selecting risk-related features, initializing the weight, and carrying out the training adjustment; calculating a user preference attenuation coefficient according to a transaction time interval, segmenting behavior characteristics, identifying behavior and preference changes of each stage, evaluating risk influences and adjusting weights; constructing an interaction data operation chain based on the data network, and adjusting behavior change rates of the user in different stages; and finally, constructing a risk conduction rule and marshalling according to historical risk characteristics, and determining a key behavior change rate. Through multi-dimensional feature analysis and behavior change rate calculation, user dynamics are accurately described, potential risk association is mined, an evaluation model is constructed in combination with historical data, risk signals can be captured in time, risk identification accuracy and timeliness are improved, and platform risk management is assisted.

The invention provides a small hydropower station safety and ecological multi-source risk dynamic assessment method and system, and belongs to the technical field of hydropower station management. Constructing a multi-dimensional risk feature tensor, including preprocessing multi-source data to obtain standardized data, extracting features from the standardized data, organizing the features according to risk types, time, space and feature four-dimensional organizations to form a four-order tensor, and performing decomposition to extract a core risk mode; constructing a conditional risk probability field, including conditional probability density estimation, risk probability field dynamic evolution model establishment and multi-source information entropy weight fusion; constructing a risk degree assessment model, including risk influence degree matrix construction, matrix spectrum analysis and risk measurement generation; according to the method, the defects of single dimension, static evaluation, high subjectivity and the like in the prior art are overcome, and accurate quantification and dynamic evaluation of the safety and ecological risk of the small hydropower station are realized.

The invention relates to the technical field of food safety risk management, in particular to an aquatic product risk analysis system based on multi-factor analysis, which comprises a pollution factor response module, a path aggregation module, a channel decision module, a pollution risk simulation module and a risk mapping collection module. According to the method, by extracting the concentration change direction of the pollution factor in the specific water area in the continuous time period and establishing the sequence combination of the concentration rising section, the time sequence behavior characteristics of the pollution factor can be identified, and the overlapping relation between the risk factor and the biological class group exposure can be effectively judged; in a standard exposure period, contact frequencies are integrated, sensitive time periods are screened, and generality factor grouping is completed by analyzing frequency coincidence conditions of a plurality of aquatic products under the same factors, so that a risk influence map can reflect a factor interaction correlation and category common inductance response relationship; and the comprehensiveness, granularity and prediction capability of risk assessment are improved in a dynamic scene.

The invention discloses a field operation video monitoring risk prevention and control method, system and device and a medium, and the method comprises the steps: analyzing the reference operation risks of the whole network over the years, and constructing a reference operation risk library according to the analysis result; establishing a first risk model through the reference operation risk library, and identifying risk influence factors; constructing a second risk model through the risk influence factors, determining weight values of the risk influence factors, outputting risk values, and performing risk grade division according to the risk values; deploying video monitoring equipment to identify operating personnel, synchronously monitoring an operation site, and analyzing and judging an abnormal condition through a pixel difference value of front and back frame images; and when the abnormal situation is judged, determining an early warning level in combination with the risk level, and giving an alarm. According to the method, historical risk data analysis and an intelligent video monitoring technology are deeply fused, so that risk assessment is fundamentally converted from static experience judgment to dynamic quantitative prediction.

The invention discloses a cross-organization collaborative project risk and progress coupling early warning method and system, and the method comprises the steps: obtaining risk data and progress data of a project and external environment data corresponding to the risk data, the risk data comprising a risk occurrence probability, and the progress data comprising a planned progress completion rate and an actual progress completion rate; determining the product of the risk occurrence probability and the risk influence degree as a risk dominant coefficient; determining an absolute value of a difference value between the actual progress completion rate and the planned progress completion rate as a progress dominant coefficient; when it is determined that the risk dominant coefficient is greater than or equal to the progress dominant coefficient, inputting the risk data and the external environment data into a preset risk-progress model to obtain first early warning information; and when it is determined that the progress dominant coefficient is greater than the risk dominant coefficient, inputting the progress data to a preset progress-risk model to obtain second early warning information. The project risk and progress early warning method is used for improving the accuracy of project risk and progress early warning.

The present invention relates to the technical field of international trade risk management, and particularly relates to an intelligent international trade risk prevention and control method and system based on multi-modal data analysis. The method comprises: collecting transaction records, logistics information, policies, laws and regulations, and dynamic market data; extracting abnormal behavior features and evaluating a transaction risk grade; in view of logistics and policy data, evaluating the degree of influence of a cross-border transportation risk, so as to obtain a comprehensive risk index; and by means of the dynamic market data, estimating an overall risk level for early warning. The method can improve the accuracy and real-time response capability of international trade risk prediction, thereby meeting intelligent risk management requirements in complex environments.

The invention relates to the technical field of maritime data processing, and discloses an intelligent photoelectric system in a complex maritime environment and a data processing method thereof. According to the method, real-time marine environment data including illumination intensity, sea wave dynamics and target object positions are collected, noise is removed through a preprocessing module, and a clean environment data set is generated; based on the data set, the identification and motion track characteristics of the target ship are extracted, high-priority and low-priority monitoring sub-areas are divided, uncovered areas are identified as background monitoring sub-areas, and a to-be-analyzed area group is formed; calculating risk difference indexes of adjacent sub-regions, and constructing a risk influence network diagram by taking the sub-regions as nodes and the indexes as edges; solving the critical path by applying a graph traversal algorithm, and deducing a data acquisition sequence; and controlling the photoelectric sensor unit of each sub-region to execute a marine environment monitoring task. The method can adapt to a complex environment, and the pertinence and orderliness of monitoring are improved.

The invention provides a distribution network equipment operation risk assessment method and system based on digital twinning, and the method comprises the steps: calculating an overall potential risk assessment value of equipment according to a pre-established future risk analysis model based on meteorological prediction based on meteorological data and historical operation data of each piece of equipment, judging whether the overall equipment has potential risks according to the overall potential risk assessment value of the equipment; based on the meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, judging whether the whole power distribution network has a risk or not according to a pre-established combined assessment model of the influence of the weather on the grid risk; if the overall equipment has the potential risk and the overall power distribution network has no risk, adjusting a risk level threshold value of the equipment, otherwise, obtaining a risk assessment result of the equipment according to a pre-established environmental factor weighted health state assessment model based on the meteorological data, the historical operation data of the equipment and the weight coefficient of the operation data; and the accuracy and timeliness of risk assessment are improved.

The invention discloses an intelligent auxiliary credit granting method for consumer finance, and relates to the technical field of monitoring analysis, and the method comprises the steps: constructing a customer initial feature set, carrying out the null value feature vector mapping analysis and intelligent completion of the customer initial feature set, generating a customer complete feature matrix, and carrying out the cross-scene model parameter migration of a risk prediction initial model. A risk prediction optimization model is obtained; performing real-time risk scanning on the customer complete feature matrix based on a risk prediction optimization model, generating a risk feature fluctuation map, extracting a key risk influence factor, performing risk conduction path simulation according to the key risk influence factor, and generating a dynamic risk early warning signal; performing credit extension decision tree reconstruction according to the dynamic risk early warning signal, generating an adaptive credit extension decision model, generating a gradient credit extension limit interval, performing decision weight fusion on the gradient credit extension limit interval and the dynamic risk early warning signal, and outputting an intelligent credit extension decision report. The method has the effect of improving the auxiliary credit extension efficiency.

The invention discloses a risk assessment method and device, electronic equipment, a storage medium and a program product, and aims to solve the problems of relatively high labor cost and relatively low risk assessment efficiency when an existing risk assessment mode is adopted. The method comprises the steps of obtaining historical claim settlement data of a position area where a target property insurance object is located; determining a conduction risk value of the historical claim settlement event to the target property insurance object according to the historical claim settlement data, and determining an aggregation risk value of the claim settlement disaster cause of the historical claim settlement event to the target property insurance object; wherein the conduction risk value is used for representing the risk influence degree of the historical claim settlement event on the target property insurance object; the aggregation risk is used for representing a nonlinear risk amplification effect generated by the interaction relationship among the multiple claim settlement disaster causes on the target property insurance object; determining an environment risk value of a position area where the target property insurance object is located; and evaluating risk information of the target property insurance object according to the conduction risk value, the aggregation risk value and the environment risk value.

The present invention discloses an industrial Internet security control method for complex attacks, relates to the field of industrial Internet, and is used to solve the problem that the current industrial Internet security protection system cannot achieve effective defense and targeted defense, and cannot dynamically adjust its own defense. The method includes: step S1, analyzing the network connection risk situation of the production link connected to the industrial Internet, and analyzing to obtain the risk equipment rate of the production link; step S2, analyzing the historical intrusion situation of the production link connected to the industrial Internet, and analyzing to obtain the risk impact value of the production link; step S3, setting a corresponding security control strategy for the production link connected to the industrial Internet based on the risk equipment rate and the risk impact value; step S4, dynamically updating the security control strategy of the production link connected to the industrial Internet according to the attack situation. The present invention realizes targeted defense and dynamic defense of the industrial Internet when facing complex attacks.

The application discloses a kind of tunnel abnormal event-oriented risk assessment method and system, it is related to tunnel traffic safety management technical field.The method includes: collecting tunnel operating state data;Generate abnormal event basic data;According to the available sensing distance and the required braking distance of abnormal event occurrence section and its associated section, generate structure sensitive coefficient, and section correction is carried out to abnormal event basic risk value;With abnormal event occurrence section as risk source section, in combination with structure sensitive correction data, propagation direction, distance attenuation and lane disturbance state calculate the propagation risk potential field value of each tunnel section;Determine abnormal event risk assessment result.The application can convert tunnel abnormal event into section-level risk assessment result from single-point alarm, improve the accuracy of tunnel abnormal event risk influence range identification and emergency disposal decision.

The application discloses a wharf risk assessment and management method based on artificial intelligence, and particularly relates to the field of wharf risk assessment and management, and is used for solving the problems that the existing wharf is difficult to quantize the influence of delay on the supply chain network level and cannot intervene in the supply chain risk in advance. The application builds a comprehensive data platform of wharf operation and supply chain cargo linkage, analyzes and predicts the future delivery window bottleneck time point based on the ship arrival density, operation carrying capacity and stacking structure, retrieves the historical supply chain interruption events and extracts the corresponding cargo and node information, tracks the conduction path of the interruption event in the supply chain network, quantizes the influence of the bottleneck on the downstream node and establishes a supply chain risk influence model, generates a supply chain risk influence graph in combination with the future bottleneck prediction, and finally builds a composite risk portrait and implements targeted risk intervention on the wharf and the supply chain node. The application realizes intelligent prediction and management of the wharf and the supply chain whole-chain risk.

According to the multi-institution cooperative risk control method and the computing device, a cross-institution transaction relationship exists among users of a plurality of participating institutions, the method is executed by any one of the participating institutions, and for the participating institution, risk influence values corresponding to the users of the participating institution are determined, performing clustering and noise adding processing on each risk influence value, and determining a plurality of clustering centers; determining a shared message of the participating mechanism according to the plurality of clustering centers, each risk influence value and the cross-mechanism transaction relationship; and transmitting the shared message to other mechanisms except the participating mechanism in the plurality of participating mechanisms, updating each risk influence value according to the shared message transmitted by the other mechanisms, and replacing the risk influence value in the shared message of each participating mechanism by using a clustering center in a scene of large-scale data sharing. The data sharing efficiency can be improved on the premise that the data security requirement and the detection effectiveness requirement are met at the same time.

To obtain an investment plan assistance system that enables determination by a user on whether a plurality of investment plans are good to be assisted more appropriately.SOLUTION: An investment plan assistance system 1 according to the present disclosure comprises: a difference calculation unit 13 which, by using the risk and cost obtained by an investment simulation corresponding to each of a reference plan which is one of a plurality of investment plans to a facility of interest and a plan to be compared which is an investment plan other than the reference plan, calculates, for each plan to be compared, the difference between the plan to be compared and the reference plan regarding risk and cost, respectively; and a display processing unit 14 which generates risk evaluation information indicating the internal ratio of the facility of interest corresponding to a combination of values of damage degree and risk impact degree of each of the reference plan and the plan to be compared, that are obtained by an investment simulation, generates first display data, including the difference and the risk evaluation information, for displaying a first display screen, and outputs the first display data to a display device 2.SELECTED DRAWING: Figure 1

This invention relates to the field of artificial intelligence technology, specifically to an AI-based big data risk assessment system, comprising the following modules: a time offset correction module, a risk factor modeling module, a mutation factor monitoring module, a risk cross-derivation module, and a prediction optimization and adjustment module. In this invention, by aligning the time density of multi-source data and unifying the reference baseline of time-series data, the temporal consistency of subsequent factor modeling is improved. By combining the trend offset rate and co-occurrence progression relationship of risk factors, the ability to identify the linkage effect between factors is enhanced. By using the trend deviation degree of cross points and the continuity of residuals, the identification of the risk impact range under the influence of multiple variables is optimized. By adopting a prediction error deviation trend combined with a weight self-adjustment mechanism, dynamic correction of model parameters is achieved, ensuring that the model adapts to the dynamic changes of the risk environment during long-term operation, enabling the risk prediction path to have adaptive repair capabilities, and improving the stability and accuracy of big data risk prediction.

This invention discloses a construction emergency early warning method and system. The method includes the following steps: collecting the three-dimensional coordinates of construction workers, using a sliding time window combined with indicators such as wavelet packet energy entropy, and generating a multi-scale motion disorder index through principal component analysis; identifying continuously unstable personnel based on the disorder index, segmenting their trajectories, and constructing a weighted graph by combining Hidden Markov Model and Building Information Model environmental parameters to calculate cognitive mismatch; mapping the cognitive mismatch to a spatial grid, calculating the local Moran index, and fusing density gradient and mechanical operation sequence resonance results to construct a propagation weight matrix; guiding the construction of a heterogeneous graph based on the weight matrix, calculating the risk impact propagation potential energy, and coupling historical disorder sequence analysis and relative entropy to obtain a group-level instability prediction value; for individuals whose prediction values ​​exceed the limit, outputting a comprehensive crisis level through motion trend prediction, spatial intersection, and shortest path algorithms. This invention improves the safety of construction operations.

The invention discloses a project cost risk assessment method and system based on big data and a medium. Relates to the technical field of engineering cost. The whole process of the whole project cost is divided into a plurality of stages through stage division of the project cost, and due to the fact that risk influence factors suffered by different stages of the project cost are different, a main influence factor and a secondary influence factor are set in each stage; and the proportion data of the main influence factors and the secondary influence factors are acquired, so that the combination results of the main influence factors and the secondary influence factors at different stages of the project cost are evaluated in a sectional manner according to the divided evaluation grades. The method achieves the more accurate prediction and evaluation of the project cost risk condition, and lays a foundation for the guidance of the project cost process.

The invention discloses a scheduling big data-oriented equipment risk assessment method and system, and the method comprises the steps: determining an equipment risk influence degree system based on the equipment importance degree and the equipment hidden danger of each piece of equipment in a power grid; the equipment risk influence degree is used for representing the possibility of causing a risk to a power grid after the equipment gives an alarm, breaks down or is abnormal; the higher the equipment risk influence degree is, the more serious the influence of the equipment on the power grid is, and the higher the risk is; establishing an equipment risk influence degree calculation model; establishing a model assessment index, and performing risk assessment on the to-be-assessed equipment according to the equipment risk influence degree calculation model; by establishing the equipment risk influence degree index system and the calculation model, high-risk equipment can be accurately identified, and the reliability and safety of power grid operation are improved.

The present invention relates to a risk warning method and system based on an automobile service supply chain, comprising the following steps: performing topological network modeling on the automobile service supply chain based on the supply chain data to obtain a topological network structure of the automobile service supply chain; identifying whether there are supply chain risk points in the topological network structure through a preset recognition algorithm, and if so, performing a risk impact analysis on the supply chain data through the supply chain risk points in the topological network structure to obtain a risk impact analysis result; performing risk scenario classification on the risk impact analysis result to obtain a risk scenario type; performing a risk warning assessment based on the risk scenario type to obtain a risk warning assessment result, and formulating a risk mitigation plan based on the risk scenario type and the risk warning assessment result, thereby solving the problem of poor communication between various links in the supply chain due to information asymmetry, making it difficult to effectively respond to emergencies.